我进行了多级逻辑回归，并使用scale函数重新调整了变量的比例。我的数据集中的变量以平均值为中心并重新定标。
以下是我的结果：
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: allbuster0 ~ lageutradeshare100 + lagtradeopenP + colonial +
lagsitc0100 + lnlaggdpp + lnlaggdpt + duration + lndist +
lagtradecontrol0 + nobust0 + nobust0sq + nobust0cb + (1 |
YearID) + (1 | partnercode) + (1 | caseid)
Data: multi.sanctions.bust0a.full@frame
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
AIC BIC logLik deviance df.resid
3304.8 3417.3 -1636.4 3272.8 8343
Scaled residuals:
Min 1Q Median 3Q Max
-3.380 -0.231 -0.110 -0.058 38.171
Random effects:
Groups Name Variance Std.Dev.
caseid (Intercept) 0.3006 0.5483
YearID (Intercept) 0.1861 0.4314
partnercode (Intercept) 0.7699 0.8774
Number of obs: 8359, groups: caseid, 93; YearID, 28; partnercode, 25
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.196786 0.324192 -12.945 < 2e-16 ***
lageutradeshare100 -0.254297 0.142502 -1.785 0.074340 .
lagtradeopenP 0.607378 0.175615 3.459 0.000543 ***
colonial1 1.356447 0.202574 6.696 2.14e-11 ***
lagsitc0100 0.300612 0.074151 4.054 5.03e-05 ***
lnlaggdpp 0.859417 0.277255 3.100 0.001937 **
lnlaggdpt -0.304214 0.089577 -3.396 0.000683 ***
duration -0.032064 0.114298 -0.281 0.779074
lndist -0.324538 0.077989 -4.161 3.16e-05 ***
lagtradecontrol0 0.009115 0.088184 0.103 0.917678
nobust0 -1.679246 0.285480 -5.882 4.05e-09 ***
nobust0sq 1.433486 0.726499 1.973 0.048480 *
nobust0cb -0.541682 0.545776 -0.992 0.320954
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
我的问题是：重新缩放数据后如何解释系数？
我感兴趣的变量是lageutradeshare100。如果不调整比例，则为百分比。现在，变量的1个单位增加了1个标准偏差，而不是变量的原始单位（在本例中为百分比）吗？